MnistCNN.py

#!/home/chaochao/anaconda2/bin
# -*- coding: utf-8 -*-
# Created by jiachaochao on 17-8-4 下午4:38
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data


def weight_variables(shape, name):
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial, name=name)


def bias_variable(shape, name):
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial, name=name)


def conv2d(x, W):
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')


def max_pool_2x2(x):
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')

x = tf.placeholder(tf.float32, shape=[None, 784], name="input")
y_ = tf.placeholder(tf.float32, shape=[None, 10], name="output")

W_conv1 = weight_variables([5, 5, 1, 32], name="W_conv1")
b_conv1 = bias_variable([32], name="b_conv1")

x_image = tf.reshape(x, [-1, 28, 28, 1])

h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)

W_conv2 = weight_variables([5, 5, 32, 64], name="W_conv2")
b_conv2 = bias_variable([64], name="b_conv2")

h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)

W_fc1 = weight_variables([7*7*64, 1024], name="W_fc1")
b_fc1 = bias_variable([1024], name="b_fc1")

h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

W_fc2 = weight_variables([1024, 10], name="W_fc2")
b_fc2 = bias_variable([10], name="b_fc2")

y_conv = tf.add(tf.matmul(h_fc1_drop, W_fc2), b_fc2, name="conv")


# 开始训练
mnist = input_data.read_data_sets("MNIST_data", one_hot=True)

cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))

train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))

accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

saver = tf.train.Saver()

with tf.Session() as sess:
    # 初始化变量
    sess.run(tf.global_variables_initializer())
    for i in range(2000):
        batch = mnist.train.next_batch(50)
        if i % 100 == 0:
            train_accuracy = accuracy.eval(feed_dict={x: batch[0], y_: batch[1], keep_prob: 1.0})
            print('step %d, training accuracy %g' % (i, train_accuracy))
        train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})

    print('test accuracy %g' % accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))
    saver.save(sess, "model/model.ckpt")